Synthèse des développements récents en analyse régionale des extrêmes hydrologiques

L’estimation adéquate des événements hydrologiques extrêmes (événements de conception) est primordiale en raison des risques importants associés à une connaissance insuffisante de ces événements. Dans les sites où l’on dispose de peu ou même d’aucune information hydrologique, on a recours aux méthodologies d’estimation régionale pour l’estimation des extrêmes hydrologiques. De nombreuses méthodologies ont été développées durant les dernières années pour améliorer l’estimation régionale de la distribution des extrêmes hydrologiques. Cet article présente une synthèse exhaustive des derniers développements en matière d’analyse hydrologique régionale. Une discussion dégage les directions principales de ces développements récents, met en évidence les défis majeurs en matière d’analyse régionale pour les années futures et évoque des pistes prometteuses de travaux de recherche afin de répondre à ces nouveaux défis.Adequate estimation of extreme hydrological variables is essential for the rational design and operation of a variety of hydraulic structures, due to the significant risk that is associated with these activities. Local frequency analysis is commonly used for the estimation of extreme hydrological events at sites where an adequate amount of data is available. However, data are usually only collected at a relatively limited number of sites. In practice, it frequently happens that little or no streamflow data is available at a site of interest (where a dam is to be constructed for example). In such cases, hydrologists can utilize a regional flood frequency procedure, relying on data available from other basins with a similar hydrologic regime.Various methods have been developed over the last few years for the regional analysis of extreme hydrological events. These regionalization approaches aim to estimate different characteristics of the extreme hydrological phenomena of interest, make different assumptions and hypotheses concerning these hydrological phenomena, rely on various types of data, and often fall under completely different theories. The present paper aims to review and classify recent developments in regional frequency analysis of extreme hydrological variables.The specific objectives of the paper are to: i) review the main recent developments in regional hydrologic modeling that have been proposed during the last few years; ii) classify these developments into different groups according to the theoretical background of the method, its specific objectives, and the characteristics of hydrological extreme phenomena it is intended to deal with; iii) propose a comprehensive discussion of these methods, and point out the hypotheses, limitations, data requirements, and potential of each one; iv) identify the new challenges facing engineers in terms of regional frequency analysis of hydrological extremes; and v) propose potential promising directions for future research work which aim to meet these new challenges.Recent developments reviewed in the present paper include improvements in classical approaches for regional delineation and for information transfer, methods combining the delineation and estimation steps, seasonality-based methods, multivariate models for regional frequency analysis, the QdF approach, non stationary models, and approaches for the combination of local and regional data. The paper provides also a discussion of the various hydrological variables treated with regional estimation methodologies, comparative studies of these methodologies, and practical tools that were developed for regional frequency analysis. It is hoped that this document will contribute towards closing the gap between theory and practice, by narrowing the wide body of literature that is available, and by providing comprehensive propositions for regional frequency analysis approaches that meet the new challenges facing hydrologic engineers

Utilisation des réseaux de neurones et de la régularisation bayésienne en modélisation de la température de l’eau en rivière

Dans ce travail, nous avons élaboré un modèle de prédiction des variations de la température d’un cours d’eau en fonction de variables climatiques, telles que la température de l’air ambiant, le débit d’eau et la quantité de précipitation reçue par le cours d’eau. Les réseaux de neurones statiques ont été utilisés pour approximer la relation entre ces différentes variables avec une erreur moyenne de 0,7 °C. Par ailleurs, nous proposons un modèle de prédiction de l’évolution de la température de l’eau à court et moyen termes pour les jours (j + i, i = 1,2,..). Deux méthodes ont été appliquées : la première, de type itérative, utilise la valeur estimée du jour j pour prédire la valeur de la température de l’eau au jour j + 1; la seconde méthode, beaucoup plus simple à mettre en oeuvre, consiste à estimer la température de tous les jours considérés en une seule fois.L’optimisation de la fonction de coût par l’algorithme de Levenberg-Marquardt, disponible dans l’outil « réseaux de neurones » de MATLAB a permis d’améliorer nettement la performance des modèles. Des résultats très satisfaisants sont alors obtenus en testant la validité du modèle par la validation croisée avec des erreurs moyennes de prédiction à sept jours de 1,5 °C.Understanding and predicting water temperatures is essential in order to help prevent or forecast high temperature problems. To attain this objective, we define in this work a model that predicts temperature variations in a small stream according to climatic variables, such as air temperature, water flow and quantity of rainfall in the river catchment. Static neural networks were used as a technique for evaluation of the relations among the various variables, with a mean error of 0.7°C.In addition, we developed a forecasting model able to estimate the short-term and mid-term variations of water temperature, i.e., to forecast the temperature of days (j+i , i=1,2…) from climatic parameters of day j. Two methods were used: the first one is iterative and uses the estimated value of day j to estimate the value of the water temperature for day j+1. The second method is much simpler, involving an estimate of the temperature of all days at once. The Levenberg-Marquardt algorithm implemented in the Matlab neural network toolbox allowed a marked improvement in the performance of the model. Very satisfactory results were then obtained by testing the validity by cross validation technique with a mean error of 1.5°C for long term prediction of 7 days

A new look at weather-related health impacts through functional regression.

A major challenge of climate change adaptation is to assess the effect of changing weather on human health. In spite of an increasing literature on the weather-related health subject, many aspect of the relationship are not known, limiting the predictive power of epidemiologic models. The present paper proposes new models to improve the performances of the currently used ones. The proposed models are based on functional data analysis (FDA), a statistical framework dealing with continuous curves instead of scalar time series. The models are applied to the temperature-related cardiovascular mortality issue in Montreal. By making use of the whole information available, the proposed models improve the prediction of cardiovascular mortality according to temperature. In addition, results shed new lights on the relationship by quantifying physiological adaptation effects. These results, not found with classical model, illustrate the potential of FDA approaches

Aggregating the response in time series regression models, applied to weather-related cardiovascular mortality.

In environmental epidemiology studies, health response data (e.g. hospitalization or mortality) are often noisy because of hospital organization and other social factors. The noise in the data can hide the true signal related to the exposure. The signal can be unveiled by performing a temporal aggregation on health data and then using it as the response in regression analysis. From aggregated series, a general methodology is introduced to account for the particularities of an aggregated response in a regression setting. This methodology can be used with usually applied regression models in weather-related health studies, such as generalized additive models (GAM) and distributed lag nonlinear models (DLNM). In particular, the residuals are modelled using an autoregressive-moving average (ARMA) model to account for the temporal dependence. The proposed methodology is illustrated by modelling the influence of temperature on cardiovascular mortality in Canada. A comparison with classical DLNMs is provided and several aggregation methods are compared. Results show that there is an increase in the fit quality when the response is aggregated, and that the estimated relationship focuses more on the outcome over several days than the classical DLNM. More precisely, among various investigated aggregation schemes, it was found that an aggregation with an asymmetric Epanechnikov kernel is more suited for studying the temperature-mortality relationship

EMD-regression for modelling multi-scale relationships, and application to weather-related cardiovascular mortality.

In a number of environmental studies, relationships between nat4ural processes are often assessed through regression analyses, using time series data. Such data are often multi-scale and non-stationary, leading to a poor accuracy of the resulting regression models and therefore to results with moderate reliability. To deal with this issue, the present paper introduces the EMD-regression methodology consisting in applying the empirical mode decomposition (EMD) algorithm on data series and then using the resulting components in regression models. The proposed methodology presents a number of advantages. First, it accounts of the issues of non-stationarity associated to the data series. Second, this approach acts as a scan for the relationship between a response variable and the predictors at different time scales, providing new insights about this relationship. To illustrate the proposed methodology it is applied to study the relationship between weather and cardiovascular mortality in Montreal, Canada. The results shed new knowledge concerning the studied relationship. For instance, they show that the humidity can cause excess mortality at the monthly time scale, which is a scale not visible in classical models. A comparison is also conducted with state of the art methods which are the generalized additive models and distributed lag models, both widely used in weather-related health studies. The comparison shows that EMD-regression achieves better prediction performances and provides more details than classical models concerning the relationship

Heat-related mortality prediction using low-frequency climate oscillation indices: Case studies of the cities of Montréal and Québec, Canada

Background: Heat-related mortality is an increasingly important public health burden that is expected to worsen with climate change. In addition to long-term trends, there are also interannual variations in heat-related mortality that are of interest for efficient planning of health services. Large-scale climate patterns have an important influence on summer weather and therefore constitute important tools to understand and predict the variations in heat-related mortality. Methods: In this article, we propose to model summer heat-related mortality using seven climate indices through a two-stage analysis using data covering the period 1981–2018 in two metropolitan areas of the province of Québec (Canada): Montréal and Québec. In the first stage, heat attributable fractions are estimated through a time series regression design and distributed lag nonlinear specification. We consider different definitions of heat. In the second stage, estimated attributable fractions are predicted using climate index curves through a functional linear regression model. Results: Results indicate that the Atlantic Multidecadal Oscillation is the best predictor of heat-related mortality in both Montréal and Québec and that it can predict up to 20% of the interannual variability. Conclusion: We found evidence that one climate index is predictive of summer heat-related mortality. More research is needed with longer time series and in different spatial contexts. The proposed analysis and the results may nonetheless help public health authorities plan for future mortality related to summer heat

Validated spectrophotometric methods for determination of Alendronate sodium in tablets through nucleophilic aromatic substitution reactions

<p>Abstract</p> <p>Background</p> <p>Alendronate (ALD) is a member of the bisphosphonate family which is used for the treatment of osteoporosis, bone metastasis, Paget's disease, hypocalcaemia associated with malignancy and other conditions that feature bone fragility. ALD is a non-chromophoric compound so its determination by conventional spectrophotometric methods is not possible. So two derivatization reactions were proposed for determination of ALD through the reaction with 4-chloro-7-nitrobenzo-2-oxa-1,3-diazole (NBD-Cl) and 2,4-dinitrofluorobenzene (DNFB) as chromogenic derivatizing reagents.</p> <p>Results</p> <p>Three simple and sensitive spectrophotometric methods are described for the determination of ALD. Method I is based on the reaction of ALD with NBD-Cl. Method II involved heat-catalyzed derivatization of ALD with DNFB, while, Method III is based on micellar-catalyzed reaction of the studied drug with DNFB at room temperature. The reactions products were measured at 472, 378 and 374 nm, for methods I, II and III, respectively. Beer's law was obeyed over the concentration ranges of 1.0-20.0, 4.0-40.0 and 1.5-30.0 μg/mL with lower limits of detection of 0.09, 1.06 and 0.06 μg/mL for Methods I, II and III, respectively. The proposed methods were applied for quantitation of the studied drug in its pure form with mean percentage recoveries of 100.47 ± 1.12, 100.17 ± 1.21 and 99.23 ± 1.26 for Methods I, II and III, respectively. Moreover the proposed methods were successfully applied for determination of ALD in different tablets. Proposals of the reactions pathways have been postulated.</p> <p>Conclusion</p> <p>The proposed spectrophotometric methods provided sensitive, specific and inexpensive analytical procedures for determination of the non-chromophoric drug alendronate either per se or in its tablet dosage forms without interference from common excipients.</p> <p>Graphical abstract</p> <p><display-formula><graphic file="1752-153X-6-25-i3.gif"/></display-formula></p

sodC-Based Real-Time PCR for Detection of Neisseria meningitidis

Real-time PCR (rt-PCR) is a widely used molecular method for detection of Neisseria meningitidis (Nm). Several rt-PCR assays for Nm target the capsule transport gene, ctrA. However, over 16% of meningococcal carriage isolates lack ctrA, rendering this target gene ineffective at identification of this sub-population of meningococcal isolates. The Cu-Zn superoxide dismutase gene, sodC, is found in Nm but not in other Neisseria species. To better identify Nm, regardless of capsule genotype or expression status, a sodC-based TaqMan rt-PCR assay was developed and validated. Standard curves revealed an average lower limit of detection of 73 genomes per reaction at cycle threshold (Ct) value of 35, with 100% average reaction efficiency and an average R2 of 0.9925. 99.7% (624/626) of Nm isolates tested were sodC-positive, with a range of average Ct values from 13.0 to 29.5. The mean sodC Ct value of these Nm isolates was 17.6±2.2 (±SD). Of the 626 Nm tested, 178 were nongroupable (NG) ctrA-negative Nm isolates, and 98.9% (176/178) of these were detected by sodC rt-PCR. The assay was 100% specific, with all 244 non-Nm isolates testing negative. Of 157 clinical specimens tested, sodC detected 25/157 Nm or 4 additional specimens compared to ctrA and 24 more than culture. Among 582 carriage specimens, sodC detected Nm in 1 more than ctrA and in 4 more than culture. This sodC rt-PCR assay is a highly sensitive and specific method for detection of Nm, especially in carriage studies where many meningococcal isolates lack capsule genes

Patient Selection in One Anastomosis/Mini Gastric Bypass—an Expert Modified Delphi Consensus

Purpose: One anastomosis/mini gastric bypass (OAGB/MGB) is up to date the third most performed obesity and metabolic procedure worldwide, which recently has been endorsed by ASMBS. The main criticisms are the risk of bile reflux, esophageal cancer, and malnutrition. Although IFSO has recognized this procedure, guidance is needed regarding selection criteria. To give clinicians a daily support in performing the right patient selection in OAGB/MGB, the aim of this paper is to generate clinical guidelines based on an expert modified Delphi consensus. Methods: A committee of 57 recognized bariatric surgeons from 24 countries created 69 statements. Modified Delphi consensus voting was performed in two rounds. An agreement/disagreement among ≥ 70.0% of the experts was considered to indicate a consensus. Results: Consensus was achieved for 56 statements. Remarkably, ≥ 90.0% of the experts felt that OAGB/MGB is an acceptable and suitable option "in patients with Body mass index (BMI) > 70, BMI > 60, BMI > 50 kg/m2 as a one-stage procedure," "as the second stage of a two-stage bariatric surgery after Sleeve Gastrectomy for BMI > 50 kg/m2 (instead of BPD/DS)," and "in patients with weight regain after restrictive procedures. No consensus was reached on the statement that OAGB/MGB is a suitable option in case of resistant Helicobacter pylori. This is likely as there is a concern that this procedure is associated with reflux and its related long-term complications including risk of cancer in the esophagus or stomach. Also no consensus reached on OAGB/MGB as conversional surgery in patients with GERD after restrictive procedures. Consensus for disagreement was predominantly achieved "in case of intestinal metaplasia of the stomach" (74.55%), "in patients with severe Gastro Esophageal Reflux Disease (GERD)(C,D)" (75.44%), "in patients with Barrett's metaplasia" (89.29%), and "in documented insulinoma" (89.47%). Conclusion: Patient selection in OAGB/MGB is still a point of discussion among experts. There was consensus that OAGB/MGB is a suitable option in elderly patients, patients with low BMI (30-35 kg/m2) with associated metabolic problems, and patients with BMIs more than 50 kg/m2 as one-stage procedure. OAGB/MGB can also be a safe procedure in vegetarian and vegan patients. Although OAGB/MGB can be a suitable procedure in patients with large hiatal hernia with concurrent hiatal hernia, it should not be offered to patients with grade C or D esophagitis or Barrett's metaplasia.info:eu-repo/semantics/publishedVersio